The new k-windows algorithm for improving the k-means clustering algorithm
Journal of Complexity
Automatic image annotation and retrieval using subspace clustering algorithm
Proceedings of the 2nd ACM international workshop on Multimedia databases
Evaluating the impact of selection noise in community-based web search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Semantic Scene Classification for Image Annotation and Retrieval
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Automatic image annotation based on wordnet and hierarchical ensembles
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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Automatic image annotation is a process of modeling a human in assigning words to images based on visual observations. It is essential as manual annotation is time consuming especially for large databases and there is no standard captioning procedure because it is based on human perception. This paper discusses implementation of automatic image annotation using K-means clustering algorithm to annotate the colors with the appropriate words by using predefined colors. Experiments are conducted to identify the number of centroids, distance measures and initialization mode for the best clustering results. A prototype of an automatic image annotation is developed and then tested using thirty-five beach scenery photographs. Results showed that annotating image using evenly-spaced initialization mode and 100 centroids measured using City-Block distance function managed to achieve a commendable 75% precision rate.